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Ensemble Methods for MiRNA Target Prediction from Expression Data.

Le TD, Zhang J, Liu L, Li J - PLoS ONE (2015)

Bottom Line: Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs.Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched.The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.

ABSTRACT

Background: microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.

Results: In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.

No MeSH data available.


Related in: MedlinePlus

Highly-confident novel interactions in the EMT, MCC, and BR51 datasets.Red nodes are miRNAs and white nodes are mRNAs. The interactions are predicted by both the ensemble method, Pearson+IDA+Lasso, and TargetScan.
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pone.0131627.g005: Highly-confident novel interactions in the EMT, MCC, and BR51 datasets.Red nodes are miRNAs and white nodes are mRNAs. The interactions are predicted by both the ensemble method, Pearson+IDA+Lasso, and TargetScan.

Mentions: As experimentally confirmed databases are still sparse, we report a set of highly-confident novel interactions for further experiment validations. The novel interactions are those predicted by Pearson+IDA+Lasso and included in TargetScan v6.2 [61], but not yet confirmed by the experimentally confirmed databases. While the ensemble method provides reliable results based on expression data, TargetScan v6.2 provides predictions on direct binding information using sequence data. Integrating the effects measured by expression levels and the sequence binding information would provide highly-confident candidates for followed-up experiments. Fig 5 shows the novel interactions in the three datasets, where red nodes are miRNAs and white nodes are novel target genes. Details of the highly-confident novel interactions are in S6 File.


Ensemble Methods for MiRNA Target Prediction from Expression Data.

Le TD, Zhang J, Liu L, Li J - PLoS ONE (2015)

Highly-confident novel interactions in the EMT, MCC, and BR51 datasets.Red nodes are miRNAs and white nodes are mRNAs. The interactions are predicted by both the ensemble method, Pearson+IDA+Lasso, and TargetScan.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482624&req=5

pone.0131627.g005: Highly-confident novel interactions in the EMT, MCC, and BR51 datasets.Red nodes are miRNAs and white nodes are mRNAs. The interactions are predicted by both the ensemble method, Pearson+IDA+Lasso, and TargetScan.
Mentions: As experimentally confirmed databases are still sparse, we report a set of highly-confident novel interactions for further experiment validations. The novel interactions are those predicted by Pearson+IDA+Lasso and included in TargetScan v6.2 [61], but not yet confirmed by the experimentally confirmed databases. While the ensemble method provides reliable results based on expression data, TargetScan v6.2 provides predictions on direct binding information using sequence data. Integrating the effects measured by expression levels and the sequence binding information would provide highly-confident candidates for followed-up experiments. Fig 5 shows the novel interactions in the three datasets, where red nodes are miRNAs and white nodes are novel target genes. Details of the highly-confident novel interactions are in S6 File.

Bottom Line: Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs.Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched.The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.

ABSTRACT

Background: microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.

Results: In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.

No MeSH data available.


Related in: MedlinePlus